*****************************************************************
* Replication directory for                                   ***
* Prime locations                                             ***
* by Gabriel M. Ahlfeldt, Thilo N.H. Albers, Kristian Behrens ***
* Published in American Economic Review: Insights             ***
*****************************************************************

This directory contains the following subfolders

cells			This folder contains grid-cell information on tradable-services employment, whether a cell can be developed,
			and the output of the C++ clustering algorithm for employment weighted big data establishments for Global Cities
			Each file contains the following variables, where vX indicates that the variable is in the Xth column 
				v1 EntityID -> Drop it
				v2 Label -> Drop it
				v3 cell row
				v4 cell col
				v5 Label -> Drop it
				v6 cell UL longitude (in radians)
				v7 cell UL latitude (in radians)
				v8 cell LR longitude (in radians)
				v9 cell LR latitude (in radians)
				v10 Label -> Drop it
				v11 cell employment (manufacturing and wholesale); zero in this data set based on big data establishments.
				v12 cell employment (non-traded services); zero in this data set based on big data establishments.
				v13 cell employment (tradable services); based on big data establishments in RAW sub folder and employment weights from US MSAs.
				v14 cell employment (public services); zero in this data set based on big data establishments.
				v15 cell employment (other); zero in this data set based on big data establishments.
				v16 cell employment (based on search terms); zero in this data set based on big data establishments.
				v17 cell total employment (this is new, so all variables starting with v17 in your code have to be +1 now)
				v18 Label -> Drop it
				v19 ClusterID (0 = no cluster)
				v20 Label -> Drop it
				v21 Unique cell ID (string)
				v22 Label -> Drop it
				v23 undevelopable = 1, developable = . We define a cell as undevelopable if it falls into water or steep-slope terrain. 
			For elevation data, we rely on the work by Danielson and Gesch (2011). These authors provide a
			global elevation dataset at the 225m level. We merge these data to our city grids and then for each grid
			point compute the slopes to all adjacent grid cells. Each grid is then assigned the highest slope within
			this set of adjacent grid midpoints. In terms of elevation, we define a grid cell as undevelopable if the
			slope between its own and the centroid of any adjacent cell exceeds 20%.
			We employ high-resolution data on water to define grid-level indicators. This is based on the global
			water database (Feng et al., 2016), which provides a 30m x 30m grid on global waterbodies. If the centroid
			of our 250m x 250m cell lies in a waterbody, it is classified as undevelopable.

RAW 			This folder contains the scraped big data establishments that enter the algorithm along with employment weights.

References

Danielson, J. J., Gesch, D. B., 2011. Global multi-resolution terrain elevation data 2010 (gmted2010).
Tech. rep., US Geological Survey.

Feng, M., Sexton, J. O., Channan, S., Townshend, J. R., 2016. A global, high-resolution (30-m) inland
water body dataset for 2000: first results of a topographicspectral classification algorithm. International
Journal of Digital Earth 9 (2), 113–133.
URL https://doi.org/10.1080/17538947.2015.1026420